Sesión 6
Ana Escoto
13/11/2021
Previo
Paquetes que hay que instalar
* findit renvars
ssc install sq, replace
ssc install moremata, replace
* search SADIBase de datos de Fundaungo:
gl datos "datos/"
gl temp "datos/temp"
Contruyendo un índice en escala de likert
No tengan miedo del cero…
Vamos a recodificar con un loop
Con estas variables nuevas, sumamos columnas
Tiene un comportamiento más continuo entre más opciones. Y sin el cero es posible que esté menos centrada, o bien, puede ser efecto de la opinión de los salvadoreños.
Introducción al análisis de secuencias
Preparación y declaración de la base
Vamos a quedarnos sólo con los que iniciaron la ronda.
gen id_miss=(id==.)
drop if id_miss==1 // nos vamos a quedar con el mismo inicio
isid correlativo
(5,003 observations deleted)
Renombramos con sufijo algunas variables de la ronda 1. Luego creamos listado de variables homologadas para cada ronda. Y nos quedamos con una base más pequeña.
renvars se1-l1 pol2 nb2 id1 , postfix(_1) // para identificador de la ronda1
gl inicio correlativo id ronda departamento se1_1-ac2_1
forvalues i=1(1)5 {
global onda_`i' se2_`i' sit1_`i' id1_`i'
}
keep $inicio $onda_1 $onda_2 $onda_3 $onda_4 $onda_5Para declarar la base la necesitamos en formato “long” por lo que vamos a hacer un reshape
reshape long se2_ sit1_ id1_, i(id) j(wave)
drop if ronda>1 // botamos el refrescamiento
recode id1 (99=.) // nuestra variable de secuencia le quitamos los missings
(j = 1 2 3 4 5)
Data Wide -> Long
-----------------------------------------------------------------------------
Number of observations 2,481 -> 12,405
Number of variables 26 -> 15
j variable (5 values) -> wave
xij variables:
se2_1 se2_2 ... se2_5 -> se2_
sit1_1 sit1_2 ... sit1_5 -> sit1_
id1_1 id1_2 ... id1_5 -> id1_
-----------------------------------------------------------------------------
(2,480 observations deleted)
(91 changes made to id1_)La base de secuencia se declara con sqset + variable de secuencia + id + onda
sqset id1 id wave, keeplongestNote: dataset has been changed due to the use of option -keeplongest-
element variable: id1_, 0 to 10
identifier variable: id, 1.010e+09 to 1.063e+09
order variable: wave, 1 to 4Si no tenemos missing no usamos la opció keeplongest. Aquí se quedará con la secuencia más larga que logre.
Análisis descriptivo de la secuencia
Tabulados
*frecuencia de las secuencias
sqtab
sqtab if se1_1==0
sqtab if se1_1==1Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
0:4 | 100 8.05 8.05
0:2 | 71 5.72 13.77
0:3 | 32 2.58 16.34
8:4 | 29 2.33 18.68
3:4 | 28 2.25 20.93
1:4 | 22 1.77 22.71
7:4 | 21 1.69 24.40
0:3 8 | 19 1.53 25.93
0 8:3 | 18 1.45 27.38
0:2 8:2 | 18 1.45 28.82
8:2 | 17 1.37 30.19
1:2 | 15 1.21 31.40
8 0 | 13 1.05 32.45
0 8 | 12 0.97 33.41
2:4 | 12 0.97 34.38
8:2 7 8 | 11 0.89 35.27
0 | 10 0.81 36.07
0:2 8 0 | 10 0.81 36.88
8 0:3 | 10 0.81 37.68
0:2 8 | 9 0.72 38.41
8 0 8:2 | 9 0.72 39.13
8 7:3 | 9 0.72 39.86
7:2 | 8 0.64 40.50
7:2 8:2 | 8 0.64 41.14
0 2 0:2 | 7 0.56 41.71
1 2:3 | 7 0.56 42.27
2:2 8:2 | 7 0.56 42.83
7:3 8 | 7 0.56 43.40
8:2 7 | 7 0.56 43.96
0 8 0:2 | 6 0.48 44.44
2 0 | 6 0.48 44.93
2:2 | 6 0.48 45.41
2:2 0:2 | 6 0.48 45.89
3:2 4 3 | 6 0.48 46.38
4:2 | 6 0.48 46.86
7 8:3 | 6 0.48 47.34
8 | 6 0.48 47.83
8 7 8:2 | 6 0.48 48.31
8:3 | 6 0.48 48.79
0 8 0 8 | 5 0.40 49.19
0 8:2 | 5 0.40 49.60
0:2 7 8 | 5 0.40 50.00
1:2 2 | 5 0.40 50.40
1:3 | 5 0.40 50.81
1:3 2 | 5 0.40 51.21
2:3 | 5 0.40 51.61
3:2 | 5 0.40 52.01
4:4 | 5 0.40 52.42
7 8 7 8 | 5 0.40 52.82
8 0 8 0 | 5 0.40 53.22
8 7 | 5 0.40 53.62
8 7:2 | 5 0.40 54.03
8:3 0 | 5 0.40 54.43
0 4 0:2 | 4 0.32 54.75
0 7:3 | 4 0.32 55.07
1 2:2 | 4 0.32 55.39
1:2 2:2 | 4 0.32 55.72
1:2 8:2 | 4 0.32 56.04
2 8 | 4 0.32 56.36
2:3 8 | 4 0.32 56.68
3:3 4 | 4 0.32 57.00
4 0:3 | 4 0.32 57.33
7 0 | 4 0.32 57.65
7 3 | 4 0.32 57.97
7 8 7 | 4 0.32 58.29
7 8:2 7 | 4 0.32 58.62
8 0 7 8 | 4 0.32 58.94
8 0:2 | 4 0.32 59.26
8:3 7 | 4 0.32 59.58
0 2 | 3 0.24 59.82
0 2 0 8 | 3 0.24 60.06
0 2 8 0 | 3 0.24 60.31
0 2:3 | 3 0.24 60.55
0 7 | 3 0.24 60.79
0 7 8 | 3 0.24 61.03
0 7 8:2 | 3 0.24 61.27
0 8 0 | 3 0.24 61.51
0 8 7 8 | 3 0.24 61.76
0 8:2 7 | 3 0.24 62.00
0:2 2 0 | 3 0.24 62.24
1 0 | 3 0.24 62.48
1 7:3 | 3 0.24 62.72
1:2 7:2 | 3 0.24 62.96
2 0 8 | 3 0.24 63.20
2 0:2 | 3 0.24 63.45
2 1 | 3 0.24 63.69
2 8:3 | 3 0.24 63.93
2:2 0 | 3 0.24 64.17
2:2 0 2 | 3 0.24 64.41
2:2 8 7 | 3 0.24 64.65
2:3 0 | 3 0.24 64.90
3:2 4 | 3 0.24 65.14
3:2 7:2 | 3 0.24 65.38
3:3 0 | 3 0.24 65.62
4 3 | 3 0.24 65.86
7 0 7 8 | 3 0.24 66.10
7 0 7:2 | 3 0.24 66.34
7 0:3 | 3 0.24 66.59
7 8 | 3 0.24 66.83
7:2 8 7 | 3 0.24 67.07
8 0 8 | 3 0.24 67.31
8 4 8:2 | 3 0.24 67.55
8 7 8 7 | 3 0.24 67.79
8 7:2 8 | 3 0.24 68.04
8:2 7:2 | 3 0.24 68.28
0 1 | 2 0.16 68.44
0 1 0:2 | 2 0.16 68.60
0 1 2:2 | 2 0.16 68.76
0 2 8 | 2 0.16 68.92
0 2 8:2 | 2 0.16 69.08
0 4:2 0 | 2 0.16 69.24
0 6 0:2 | 2 0.16 69.40
0 7 8 0 | 2 0.16 69.57
0 7 8 7 | 2 0.16 69.73
0 7:2 | 2 0.16 69.89
0 7:2 8 | 2 0.16 70.05
0 8 7 | 2 0.16 70.21
0:2 10 | 2 0.16 70.37
0:2 7 | 2 0.16 70.53
0:2 7 0 | 2 0.16 70.69
0:2 7:2 | 2 0.16 70.85
0:2 8 4 | 2 0.16 71.01
0:3 4 | 2 0.16 71.18
1 0:3 | 2 0.16 71.34
1 2 | 2 0.16 71.50
1 2 0 2 | 2 0.16 71.66
1 2 0:2 | 2 0.16 71.82
1:2 2 1 | 2 0.16 71.98
1:2 6 5 | 2 0.16 72.14
1:3 0 | 2 0.16 72.30
1:3 8 | 2 0.16 72.46
2 0 2 0 | 2 0.16 72.62
2 0 7 8 | 2 0.16 72.79
2 0 8:2 | 2 0.16 72.95
2 0:2 8 | 2 0.16 73.11
2 0:3 | 2 0.16 73.27
2 1:2 2 | 2 0.16 73.43
2 7 | 2 0.16 73.59
2 8:2 | 2 0.16 73.75
2:2 0 8 | 2 0.16 73.91
3 4 0 8 | 2 0.16 74.07
3 4:2 | 2 0.16 74.24
3 4:3 | 2 0.16 74.40
3 7:3 | 2 0.16 74.56
3 8 | 2 0.16 74.72
3 8:3 | 2 0.16 74.88
3:2 4:2 | 2 0.16 75.04
3:3 | 2 0.16 75.20
4 0:2 | 2 0.16 75.36
4 1:2 | 2 0.16 75.52
4 3 4:2 | 2 0.16 75.68
4 8 0:2 | 2 0.16 75.85
4:2 7:2 | 2 0.16 76.01
4:3 0 | 2 0.16 76.17
6:2 8:2 | 2 0.16 76.33
7 | 2 0.16 76.49
7 0 7 | 2 0.16 76.65
7 0:2 8 | 2 0.16 76.81
7 8 7:2 | 2 0.16 76.97
7 8:2 | 2 0.16 77.13
7:2 0:2 | 2 0.16 77.29
7:3 | 2 0.16 77.46
8 0 7 0 | 2 0.16 77.62
8 0:2 8 | 2 0.16 77.78
8 2 8:2 | 2 0.16 77.94
8 3 | 2 0.16 78.10
8 4 8 7 | 2 0.16 78.26
8 4:3 | 2 0.16 78.42
8 7:2 0 | 2 0.16 78.58
8:2 0 7 | 2 0.16 78.74
8:2 0:2 | 2 0.16 78.90
0 1 0 | 1 0.08 78.99
0 1 0 8 | 1 0.08 79.07
0 1 2 | 1 0.08 79.15
0 1 8 | 1 0.08 79.23
0 10 0 8 | 1 0.08 79.31
0 10 0:2 | 1 0.08 79.39
0 10 7 | 1 0.08 79.47
0 10 7 8 | 1 0.08 79.55
0 10 8:2 | 1 0.08 79.63
0 2 0 | 1 0.08 79.71
0 2 0 1 | 1 0.08 79.79
0 2 0 2 | 1 0.08 79.87
0 2 0 6 | 1 0.08 79.95
0 2 1 | 1 0.08 80.03
0 2 6 2 | 1 0.08 80.11
0 2 7 8 | 1 0.08 80.19
0 2:2 | 1 0.08 80.27
0 2:2 0 | 1 0.08 80.35
0 2:2 8 | 1 0.08 80.43
0 3 | 1 0.08 80.52
0 3 0 4 | 1 0.08 80.60
0 3 0 7 | 1 0.08 80.68
0 3 0:2 | 1 0.08 80.76
0 3 7 | 1 0.08 80.84
0 3 8 0 | 1 0.08 80.92
0 3 8 7 | 1 0.08 81.00
0 3 8:2 | 1 0.08 81.08
0 3:2 4 | 1 0.08 81.16
0 3:3 | 1 0.08 81.24
0 4 | 1 0.08 81.32
0 4 0 | 1 0.08 81.40
0 4 0 8 | 1 0.08 81.48
0 4 3 | 1 0.08 81.56
0 4 3:2 | 1 0.08 81.64
0 4 7 0 | 1 0.08 81.72
0 4 8 | 1 0.08 81.80
0 4 8 7 | 1 0.08 81.88
0 4:2 | 1 0.08 81.96
0 5 7:2 | 1 0.08 82.05
0 5 8 0 | 1 0.08 82.13
0 6 | 1 0.08 82.21
0 6 0 7 | 1 0.08 82.29
0 6 0 8 | 1 0.08 82.37
0 6 8:2 | 1 0.08 82.45
0 7 0 | 1 0.08 82.53
0 7 0 8 | 1 0.08 82.61
0 7 8 6 | 1 0.08 82.69
0 7:2 0 | 1 0.08 82.77
0 8 3 5 | 1 0.08 82.85
0 8 5 8 | 1 0.08 82.93
0 8 6 0 | 1 0.08 83.01
0 8 6:2 | 1 0.08 83.09
0 8 7:2 | 1 0.08 83.17
0 8:2 0 | 1 0.08 83.25
0 8:2 6 | 1 0.08 83.33
0:2 10 0 | 1 0.08 83.41
0:2 2 8 | 1 0.08 83.49
0:2 3:2 | 1 0.08 83.57
0:2 8 2 | 1 0.08 83.66
0:2 8 7 | 1 0.08 83.74
0:2 9 6 | 1 0.08 83.82
0:3 1 | 1 0.08 83.90
0:3 10 | 1 0.08 83.98
0:3 2 | 1 0.08 84.06
0:3 7 | 1 0.08 84.14
1 | 1 0.08 84.22
1 0 2 | 1 0.08 84.30
1 0 8 0 | 1 0.08 84.38
1 0 8:2 | 1 0.08 84.46
1 2 0 | 1 0.08 84.54
1 2 0 8 | 1 0.08 84.62
1 2 1 2 | 1 0.08 84.70
1 2 1:2 | 1 0.08 84.78
1 2 4 8 | 1 0.08 84.86
1 2 7 2 | 1 0.08 84.94
1 2 8 2 | 1 0.08 85.02
1 2 8:2 | 1 0.08 85.10
1 2:2 1 | 1 0.08 85.19
1 4 3:2 | 1 0.08 85.27
1 4 7 8 | 1 0.08 85.35
1 7 1 6 | 1 0.08 85.43
1 7:2 8 | 1 0.08 85.51
1 8 | 1 0.08 85.59
1 8 7 0 | 1 0.08 85.67
1 8 7:2 | 1 0.08 85.75
10 | 1 0.08 85.83
10 0 | 1 0.08 85.91
10 0 4 0 | 1 0.08 85.99
10 0 8 | 1 0.08 86.07
10 0:2 6 | 1 0.08 86.15
10 0:2 8 | 1 0.08 86.23
10 0:3 | 1 0.08 86.31
10 7 0 8 | 1 0.08 86.39
10:2 0 8 | 1 0.08 86.47
10:2 1:2 | 1 0.08 86.55
1:2 0 1 | 1 0.08 86.63
1:2 0 2 | 1 0.08 86.71
1:2 0:2 | 1 0.08 86.80
1:2 2 0 | 1 0.08 86.88
1:2 2 8 | 1 0.08 86.96
1:2 7 | 1 0.08 87.04
1:2 7 1 | 1 0.08 87.12
1:2 7 2 | 1 0.08 87.20
1:2 8 | 1 0.08 87.28
1:2 8 0 | 1 0.08 87.36
1:2 8 2 | 1 0.08 87.44
1:3 7 | 1 0.08 87.52
2 | 1 0.08 87.60
2 0 2 | 1 0.08 87.68
2 0 4 0 | 1 0.08 87.76
2 1 2 | 1 0.08 87.84
2 1 2 1 | 1 0.08 87.92
2 1 2:2 | 1 0.08 88.00
2 1 6 | 1 0.08 88.08
2 1 7 8 | 1 0.08 88.16
2 1 8 0 | 1 0.08 88.24
2 1 8 6 | 1 0.08 88.33
2 1 8:2 | 1 0.08 88.41
2 10 | 1 0.08 88.49
2 10 0 | 1 0.08 88.57
2 1:2 8 | 1 0.08 88.65
2 3 4 | 1 0.08 88.73
2 4 0 | 1 0.08 88.81
2 4 1 2 | 1 0.08 88.89
2 4 3 8 | 1 0.08 88.97
2 4:2 3 | 1 0.08 89.05
2 5 | 1 0.08 89.13
2 6 2 8 | 1 0.08 89.21
2 6 7 8 | 1 0.08 89.29
2 7:3 | 1 0.08 89.37
2 8 0 8 | 1 0.08 89.45
2 8 0:2 | 1 0.08 89.53
2 8 7 | 1 0.08 89.61
2 8 7:2 | 1 0.08 89.69
2:2 1 2 | 1 0.08 89.77
2:2 1 7 | 1 0.08 89.86
2:2 6 2 | 1 0.08 89.94
2:2 7 | 1 0.08 90.02
2:2 7 8 | 1 0.08 90.10
2:2 7:2 | 1 0.08 90.18
2:2 8 | 1 0.08 90.26
2:2 8 2 | 1 0.08 90.34
2:3 1 | 1 0.08 90.42
3 | 1 0.08 90.50
3 0 | 1 0.08 90.58
3 0:2 | 1 0.08 90.66
3 1 | 1 0.08 90.74
3 4 | 1 0.08 90.82
3 4 0 | 1 0.08 90.90
3 4 0 4 | 1 0.08 90.98
3 4 0:2 | 1 0.08 91.06
3 4 3 4 | 1 0.08 91.14
3 4 8 0 | 1 0.08 91.22
3 4 8:2 | 1 0.08 91.30
3 8 0 | 1 0.08 91.38
3:2 0 4 | 1 0.08 91.47
3:2 0:2 | 1 0.08 91.55
3:2 4 8 | 1 0.08 91.63
3:2 7 3 | 1 0.08 91.71
3:3 1 | 1 0.08 91.79
3:3 8 | 1 0.08 91.87
4 | 1 0.08 91.95
4 0 | 1 0.08 92.03
4 0 10 0 | 1 0.08 92.11
4 0 8 | 1 0.08 92.19
4 0:2 4 | 1 0.08 92.27
4 3 0:2 | 1 0.08 92.35
4 3 4 0 | 1 0.08 92.43
4 3 4 3 | 1 0.08 92.51
4 3 8 | 1 0.08 92.59
4 3 8 7 | 1 0.08 92.67
4 3 8:2 | 1 0.08 92.75
4 3:2 | 1 0.08 92.83
4 3:2 4 | 1 0.08 92.91
4 3:3 | 1 0.08 93.00
4 6:3 | 1 0.08 93.08
4 7 | 1 0.08 93.16
4 7 0 | 1 0.08 93.24
4 7:2 | 1 0.08 93.32
4 7:3 | 1 0.08 93.40
4 8 | 1 0.08 93.48
4 8 0 8 | 1 0.08 93.56
4 8 4 8 | 1 0.08 93.64
4 8:2 | 1 0.08 93.72
4:2 0 4 | 1 0.08 93.80
4:2 0 8 | 1 0.08 93.88
4:2 3 6 | 1 0.08 93.96
4:2 3:2 | 1 0.08 94.04
4:2 8 4 | 1 0.08 94.12
4:2 8:2 | 1 0.08 94.20
4:3 | 1 0.08 94.28
4:3 8 | 1 0.08 94.36
5 0 | 1 0.08 94.44
5 4 | 1 0.08 94.52
5 4 3 4 | 1 0.08 94.61
5 4:2 8 | 1 0.08 94.69
5 7 | 1 0.08 94.77
5 7 5 | 1 0.08 94.85
5 8 0 7 | 1 0.08 94.93
5 8 7:2 | 1 0.08 95.01
5 8:2 | 1 0.08 95.09
6 | 1 0.08 95.17
6 0 | 1 0.08 95.25
6 0:2 | 1 0.08 95.33
6 0:2 8 | 1 0.08 95.41
6 2:2 8 | 1 0.08 95.49
6 5 7 | 1 0.08 95.57
6 5 7 1 | 1 0.08 95.65
6 7 8:2 | 1 0.08 95.73
6 7:2 8 | 1 0.08 95.81
6 8 0 8 | 1 0.08 95.89
6 8 6 8 | 1 0.08 95.97
6 8 7 8 | 1 0.08 96.05
6 8:2 | 1 0.08 96.14
6 8:2 6 | 1 0.08 96.22
6:2 7 | 1 0.08 96.30
6:2 8 6 | 1 0.08 96.38
6:2 8 7 | 1 0.08 96.46
7 0 8:2 | 1 0.08 96.54
7 1 | 1 0.08 96.62
7 2 8 1 | 1 0.08 96.70
7 3 8 4 | 1 0.08 96.78
7 4 0:2 | 1 0.08 96.86
7 4 3 | 1 0.08 96.94
7 4 3 4 | 1 0.08 97.02
7 4 7:2 | 1 0.08 97.10
7 6 8:2 | 1 0.08 97.18
7 6:2 8 | 1 0.08 97.26
7 8 0:2 | 1 0.08 97.34
7 8:2 0 | 1 0.08 97.42
7:3 6 | 1 0.08 97.50
8 0 4 | 1 0.08 97.58
8 0 4:2 | 1 0.08 97.67
8 0 7:2 | 1 0.08 97.75
8 1 0 2 | 1 0.08 97.83
8 1 2 8 | 1 0.08 97.91
8 10 8 0 | 1 0.08 97.99
8 1:2 2 | 1 0.08 98.07
8 1:3 | 1 0.08 98.15
8 2 0 2 | 1 0.08 98.23
8 2 6 8 | 1 0.08 98.31
8 2:3 | 1 0.08 98.39
8 3 7:2 | 1 0.08 98.47
8 3 8:2 | 1 0.08 98.55
8 3:2 | 1 0.08 98.63
8 6 2 | 1 0.08 98.71
8 6 7 5 | 1 0.08 98.79
8 6 8:2 | 1 0.08 98.87
8 7 4 8 | 1 0.08 98.95
8 7 6 8 | 1 0.08 99.03
8 7 8 | 1 0.08 99.11
8 7 8 0 | 1 0.08 99.19
8:2 0 2 | 1 0.08 99.28
8:2 0 8 | 1 0.08 99.36
8:2 4 | 1 0.08 99.44
8:2 5 6 | 1 0.08 99.52
8:2 6 8 | 1 0.08 99.60
8:2 7 0 | 1 0.08 99.68
8:2 7 3 | 1 0.08 99.76
8:3 3 | 1 0.08 99.84
9 0:3 | 1 0.08 99.92
9 8 9 0 | 1 0.08 100.00
------------+-----------------------------------
Total | 1,242 100.00
Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
0:4 | 42 7.18 7.18
0:2 | 31 5.30 12.48
0:3 | 15 2.56 15.04
3:4 | 13 2.22 17.26
8:4 | 13 2.22 19.49
0 8:3 | 11 1.88 21.37
7:4 | 11 1.88 23.25
8:2 | 10 1.71 24.96
0 8 | 7 1.20 26.15
1:2 | 7 1.20 27.35
1:4 | 6 1.03 28.38
2:4 | 6 1.03 29.40
8 0 | 6 1.03 30.43
8 0 8:2 | 6 1.03 31.45
0 | 5 0.85 32.31
0:2 8 | 5 0.85 33.16
0:2 8:2 | 5 0.85 34.02
0:3 8 | 5 0.85 34.87
2:2 8:2 | 5 0.85 35.73
7:2 | 5 0.85 36.58
7:2 8:2 | 5 0.85 37.44
8:2 7 8 | 5 0.85 38.29
8:3 | 5 0.85 39.15
1 2:2 | 4 0.68 39.83
2:3 | 4 0.68 40.51
4:4 | 4 0.68 41.20
7 0 | 4 0.68 41.88
7 8:3 | 4 0.68 42.56
8 | 4 0.68 43.25
8 0:2 | 4 0.68 43.93
8:2 7 | 4 0.68 44.62
0 2 0:2 | 3 0.51 45.13
0 7:3 | 3 0.51 45.64
0 8:2 7 | 3 0.51 46.15
0:2 7 8 | 3 0.51 46.67
0:2 8 0 | 3 0.51 47.18
1 2:3 | 3 0.51 47.69
1:2 2 | 3 0.51 48.21
1:2 8:2 | 3 0.51 48.72
1:3 2 | 3 0.51 49.23
2 0 | 3 0.51 49.74
2 0:2 | 3 0.51 50.26
2:2 | 3 0.51 50.77
3:2 4 3 | 3 0.51 51.28
3:3 0 | 3 0.51 51.79
3:3 4 | 3 0.51 52.31
4:2 | 3 0.51 52.82
7 3 | 3 0.51 53.33
8 0 8 0 | 3 0.51 53.85
8 7 8 7 | 3 0.51 54.36
8 7:2 | 3 0.51 54.87
8 7:3 | 3 0.51 55.38
8:2 7:2 | 3 0.51 55.90
0 1 2:2 | 2 0.34 56.24
0 2 | 2 0.34 56.58
0 2:3 | 2 0.34 56.92
0 7:2 | 2 0.34 57.26
0 8 0 8 | 2 0.34 57.61
0 8 0:2 | 2 0.34 57.95
0 8 7 8 | 2 0.34 58.29
0 8:2 | 2 0.34 58.63
0:2 2 0 | 2 0.34 58.97
0:2 7:2 | 2 0.34 59.32
1 7:3 | 2 0.34 59.66
1:2 2:2 | 2 0.34 60.00
1:2 6 5 | 2 0.34 60.34
1:2 7:2 | 2 0.34 60.68
1:3 | 2 0.34 61.03
2 1 | 2 0.34 61.37
2 8:3 | 2 0.34 61.71
2:2 8 7 | 2 0.34 62.05
2:3 8 | 2 0.34 62.39
3 4 0 8 | 2 0.34 62.74
3 8:3 | 2 0.34 63.08
3:2 | 2 0.34 63.42
3:2 4:2 | 2 0.34 63.76
3:2 7:2 | 2 0.34 64.10
4 0:3 | 2 0.34 64.44
4 3 4:2 | 2 0.34 64.79
4:3 0 | 2 0.34 65.13
6:2 8:2 | 2 0.34 65.47
7 | 2 0.34 65.81
7 0 7 8 | 2 0.34 66.15
7 0 7:2 | 2 0.34 66.50
7 8 7 | 2 0.34 66.84
7:2 0:2 | 2 0.34 67.18
7:2 8 7 | 2 0.34 67.52
7:3 8 | 2 0.34 67.86
8 0 7 0 | 2 0.34 68.21
8 0 7 8 | 2 0.34 68.55
8 0 8 | 2 0.34 68.89
8 0:3 | 2 0.34 69.23
8 3 | 2 0.34 69.57
8 4:3 | 2 0.34 69.91
8 7 | 2 0.34 70.26
8:2 0 7 | 2 0.34 70.60
8:3 0 | 2 0.34 70.94
8:3 7 | 2 0.34 71.28
0 1 | 1 0.17 71.45
0 1 0 | 1 0.17 71.62
0 10 0 8 | 1 0.17 71.79
0 10 0:2 | 1 0.17 71.97
0 10 7 8 | 1 0.17 72.14
0 10 8:2 | 1 0.17 72.31
0 2 0 | 1 0.17 72.48
0 2 0 8 | 1 0.17 72.65
0 2 1 | 1 0.17 72.82
0 2 8 0 | 1 0.17 72.99
0 2 8:2 | 1 0.17 73.16
0 2:2 | 1 0.17 73.33
0 2:2 8 | 1 0.17 73.50
0 3 | 1 0.17 73.68
0 3 0 7 | 1 0.17 73.85
0 3 0:2 | 1 0.17 74.02
0 3 8 0 | 1 0.17 74.19
0 3 8 7 | 1 0.17 74.36
0 3 8:2 | 1 0.17 74.53
0 4 | 1 0.17 74.70
0 4 0 | 1 0.17 74.87
0 4 0 8 | 1 0.17 75.04
0 4 3:2 | 1 0.17 75.21
0 4 7 0 | 1 0.17 75.38
0 4:2 0 | 1 0.17 75.56
0 5 7:2 | 1 0.17 75.73
0 6 0 8 | 1 0.17 75.90
0 6 8:2 | 1 0.17 76.07
0 7 | 1 0.17 76.24
0 7 0 | 1 0.17 76.41
0 7 0 8 | 1 0.17 76.58
0 7 8 | 1 0.17 76.75
0 7 8 7 | 1 0.17 76.92
0 7 8:2 | 1 0.17 77.09
0 7:2 0 | 1 0.17 77.26
0 7:2 8 | 1 0.17 77.44
0 8 0 | 1 0.17 77.61
0 8 3 5 | 1 0.17 77.78
0 8 5 8 | 1 0.17 77.95
0 8 6:2 | 1 0.17 78.12
0 8 7 | 1 0.17 78.29
0 8:2 0 | 1 0.17 78.46
0 8:2 6 | 1 0.17 78.63
0:2 10 | 1 0.17 78.80
0:2 10 0 | 1 0.17 78.97
0:2 7 | 1 0.17 79.15
0:2 7 0 | 1 0.17 79.32
0:2 9 6 | 1 0.17 79.49
0:3 1 | 1 0.17 79.66
0:3 2 | 1 0.17 79.83
0:3 7 | 1 0.17 80.00
1 | 1 0.17 80.17
1 0 | 1 0.17 80.34
1 2 | 1 0.17 80.51
1 2 0 8 | 1 0.17 80.68
1 2 1 2 | 1 0.17 80.85
1 2 4 8 | 1 0.17 81.03
1 2 8 2 | 1 0.17 81.20
1 2 8:2 | 1 0.17 81.37
1 4 7 8 | 1 0.17 81.54
1 7 1 6 | 1 0.17 81.71
1 8 7:2 | 1 0.17 81.88
10 0 | 1 0.17 82.05
10 0 4 0 | 1 0.17 82.22
10 0 8 | 1 0.17 82.39
10:2 0 8 | 1 0.17 82.56
1:2 2 0 | 1 0.17 82.74
1:2 2 8 | 1 0.17 82.91
1:2 8 | 1 0.17 83.08
1:3 0 | 1 0.17 83.25
1:3 8 | 1 0.17 83.42
2 0 2 0 | 1 0.17 83.59
2 0 4 0 | 1 0.17 83.76
2 0 8 | 1 0.17 83.93
2 0:2 8 | 1 0.17 84.10
2 1 2 | 1 0.17 84.27
2 1 2 1 | 1 0.17 84.44
2 1 6 | 1 0.17 84.62
2 10 | 1 0.17 84.79
2 1:2 2 | 1 0.17 84.96
2 1:2 8 | 1 0.17 85.13
2 3 4 | 1 0.17 85.30
2 4 1 2 | 1 0.17 85.47
2 4 3 8 | 1 0.17 85.64
2 4:2 3 | 1 0.17 85.81
2 5 | 1 0.17 85.98
2 6 7 8 | 1 0.17 86.15
2 7 | 1 0.17 86.32
2 8 | 1 0.17 86.50
2 8 0 8 | 1 0.17 86.67
2 8 7 | 1 0.17 86.84
2:2 0 | 1 0.17 87.01
2:2 0 2 | 1 0.17 87.18
2:2 0 8 | 1 0.17 87.35
2:2 0:2 | 1 0.17 87.52
2:2 8 2 | 1 0.17 87.69
2:3 0 | 1 0.17 87.86
2:3 1 | 1 0.17 88.03
3 4 0 4 | 1 0.17 88.21
3 4 8:2 | 1 0.17 88.38
3 4:2 | 1 0.17 88.55
3 4:3 | 1 0.17 88.72
3 7:3 | 1 0.17 88.89
3:2 0:2 | 1 0.17 89.06
3:2 4 8 | 1 0.17 89.23
3:3 8 | 1 0.17 89.40
4 | 1 0.17 89.57
4 0 | 1 0.17 89.74
4 1:2 | 1 0.17 89.91
4 3 | 1 0.17 90.09
4 3 0:2 | 1 0.17 90.26
4 3 4 0 | 1 0.17 90.43
4 3 8 | 1 0.17 90.60
4 3 8 7 | 1 0.17 90.77
4 3:2 | 1 0.17 90.94
4 6:3 | 1 0.17 91.11
4 7:2 | 1 0.17 91.28
4 8 0 8 | 1 0.17 91.45
4 8 0:2 | 1 0.17 91.62
4 8:2 | 1 0.17 91.79
4:2 0 4 | 1 0.17 91.97
4:2 3:2 | 1 0.17 92.14
4:2 8 4 | 1 0.17 92.31
4:3 | 1 0.17 92.48
5 0 | 1 0.17 92.65
5 4 3 4 | 1 0.17 92.82
5 4:2 8 | 1 0.17 92.99
5 7 | 1 0.17 93.16
5 7 5 | 1 0.17 93.33
6 | 1 0.17 93.50
6 0:2 | 1 0.17 93.68
6 2:2 8 | 1 0.17 93.85
6 5 7 1 | 1 0.17 94.02
6 7 8:2 | 1 0.17 94.19
6 7:2 8 | 1 0.17 94.36
6 8 0 8 | 1 0.17 94.53
6 8 6 8 | 1 0.17 94.70
6:2 7 | 1 0.17 94.87
6:2 8 6 | 1 0.17 95.04
7 0:2 8 | 1 0.17 95.21
7 3 8 4 | 1 0.17 95.38
7 4 3 | 1 0.17 95.56
7 4 7:2 | 1 0.17 95.73
7 8 0:2 | 1 0.17 95.90
7 8 7 8 | 1 0.17 96.07
7 8:2 | 1 0.17 96.24
7 8:2 7 | 1 0.17 96.41
7:3 | 1 0.17 96.58
8 0 4 | 1 0.17 96.75
8 1 2 8 | 1 0.17 96.92
8 10 8 0 | 1 0.17 97.09
8 2 0 2 | 1 0.17 97.26
8 2 8:2 | 1 0.17 97.44
8 3 7:2 | 1 0.17 97.61
8 4 8 7 | 1 0.17 97.78
8 4 8:2 | 1 0.17 97.95
8 6 2 | 1 0.17 98.12
8 6 8:2 | 1 0.17 98.29
8 7 8 | 1 0.17 98.46
8 7 8 0 | 1 0.17 98.63
8 7 8:2 | 1 0.17 98.80
8 7:2 0 | 1 0.17 98.97
8 7:2 8 | 1 0.17 99.15
8:2 0 2 | 1 0.17 99.32
8:2 0 8 | 1 0.17 99.49
8:2 0:2 | 1 0.17 99.66
8:2 5 6 | 1 0.17 99.83
8:3 3 | 1 0.17 100.00
------------+-----------------------------------
Total | 585 100.00
Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
0:4 | 58 8.83 8.83
0:2 | 40 6.09 14.92
0:3 | 17 2.59 17.50
1:4 | 16 2.44 19.94
8:4 | 16 2.44 22.37
3:4 | 15 2.28 24.66
0:3 8 | 14 2.13 26.79
0:2 8:2 | 13 1.98 28.77
7:4 | 10 1.52 30.29
1:2 | 8 1.22 31.51
8 0:3 | 8 1.22 32.72
0 8:3 | 7 1.07 33.79
0:2 8 0 | 7 1.07 34.86
8 0 | 7 1.07 35.92
8:2 | 7 1.07 36.99
2:4 | 6 0.91 37.90
8 7:3 | 6 0.91 38.81
8:2 7 8 | 6 0.91 39.73
0 | 5 0.76 40.49
0 8 | 5 0.76 41.25
2:2 0:2 | 5 0.76 42.01
7:3 8 | 5 0.76 42.77
8 7 8:2 | 5 0.76 43.53
0 2 0:2 | 4 0.61 44.14
0 4 0:2 | 4 0.61 44.75
0 8 0:2 | 4 0.61 45.36
0:2 8 | 4 0.61 45.97
1 2:3 | 4 0.61 46.58
7 8 7 8 | 4 0.61 47.18
0 8 0 8 | 3 0.46 47.64
0 8:2 | 3 0.46 48.10
1:3 | 3 0.46 48.55
2 0 | 3 0.46 49.01
2 8 | 3 0.46 49.47
2:2 | 3 0.46 49.92
3:2 | 3 0.46 50.38
3:2 4 | 3 0.46 50.84
3:2 4 3 | 3 0.46 51.29
4:2 | 3 0.46 51.75
7 0:3 | 3 0.46 52.21
7 8 | 3 0.46 52.66
7 8:2 7 | 3 0.46 53.12
7:2 | 3 0.46 53.58
7:2 8:2 | 3 0.46 54.03
8 0 8:2 | 3 0.46 54.49
8 7 | 3 0.46 54.95
8:2 7 | 3 0.46 55.40
8:3 0 | 3 0.46 55.86
0 1 0:2 | 2 0.30 56.16
0 2 0 8 | 2 0.30 56.47
0 2 8 | 2 0.30 56.77
0 2 8 0 | 2 0.30 57.08
0 6 0:2 | 2 0.30 57.38
0 7 | 2 0.30 57.69
0 7 8 | 2 0.30 57.99
0 7 8 0 | 2 0.30 58.30
0 7 8:2 | 2 0.30 58.60
0 8 0 | 2 0.30 58.90
0:2 7 8 | 2 0.30 59.21
0:2 8 4 | 2 0.30 59.51
0:3 4 | 2 0.30 59.82
1 0 | 2 0.30 60.12
1 0:3 | 2 0.30 60.43
1 2 0 2 | 2 0.30 60.73
1 2 0:2 | 2 0.30 61.04
1:2 2 | 2 0.30 61.34
1:2 2 1 | 2 0.30 61.64
1:2 2:2 | 2 0.30 61.95
1:3 2 | 2 0.30 62.25
2 0 7 8 | 2 0.30 62.56
2 0 8 | 2 0.30 62.86
2 0 8:2 | 2 0.30 63.17
2 0:3 | 2 0.30 63.47
2 8:2 | 2 0.30 63.77
2:2 0 | 2 0.30 64.08
2:2 0 2 | 2 0.30 64.38
2:2 8:2 | 2 0.30 64.69
2:3 0 | 2 0.30 64.99
2:3 8 | 2 0.30 65.30
3 8 | 2 0.30 65.60
3:3 | 2 0.30 65.91
4 0:2 | 2 0.30 66.21
4 0:3 | 2 0.30 66.51
4 3 | 2 0.30 66.82
4:2 7:2 | 2 0.30 67.12
7 0 7 | 2 0.30 67.43
7 8 7 | 2 0.30 67.73
7 8 7:2 | 2 0.30 68.04
7 8:3 | 2 0.30 68.34
8 | 2 0.30 68.65
8 0 7 8 | 2 0.30 68.95
8 0 8 0 | 2 0.30 69.25
8 0:2 8 | 2 0.30 69.56
8 4 8:2 | 2 0.30 69.86
8 7:2 | 2 0.30 70.17
8 7:2 8 | 2 0.30 70.47
8:3 7 | 2 0.30 70.78
0 1 | 1 0.15 70.93
0 1 0 8 | 1 0.15 71.08
0 1 2 | 1 0.15 71.23
0 1 8 | 1 0.15 71.39
0 10 7 | 1 0.15 71.54
0 2 | 1 0.15 71.69
0 2 0 1 | 1 0.15 71.84
0 2 0 2 | 1 0.15 71.99
0 2 0 6 | 1 0.15 72.15
0 2 6 2 | 1 0.15 72.30
0 2 7 8 | 1 0.15 72.45
0 2 8:2 | 1 0.15 72.60
0 2:2 0 | 1 0.15 72.75
0 2:3 | 1 0.15 72.91
0 3 0 4 | 1 0.15 73.06
0 3 7 | 1 0.15 73.21
0 3:2 4 | 1 0.15 73.36
0 3:3 | 1 0.15 73.52
0 4 3 | 1 0.15 73.67
0 4 8 | 1 0.15 73.82
0 4 8 7 | 1 0.15 73.97
0 4:2 | 1 0.15 74.12
0 4:2 0 | 1 0.15 74.28
0 5 8 0 | 1 0.15 74.43
0 6 | 1 0.15 74.58
0 6 0 7 | 1 0.15 74.73
0 7 8 6 | 1 0.15 74.89
0 7 8 7 | 1 0.15 75.04
0 7:2 8 | 1 0.15 75.19
0 7:3 | 1 0.15 75.34
0 8 6 0 | 1 0.15 75.49
0 8 7 | 1 0.15 75.65
0 8 7 8 | 1 0.15 75.80
0 8 7:2 | 1 0.15 75.95
0:2 10 | 1 0.15 76.10
0:2 2 0 | 1 0.15 76.26
0:2 2 8 | 1 0.15 76.41
0:2 3:2 | 1 0.15 76.56
0:2 7 | 1 0.15 76.71
0:2 7 0 | 1 0.15 76.86
0:2 8 2 | 1 0.15 77.02
0:2 8 7 | 1 0.15 77.17
0:3 10 | 1 0.15 77.32
1 0 2 | 1 0.15 77.47
1 0 8 0 | 1 0.15 77.63
1 0 8:2 | 1 0.15 77.78
1 2 | 1 0.15 77.93
1 2 0 | 1 0.15 78.08
1 2 1:2 | 1 0.15 78.23
1 2 7 2 | 1 0.15 78.39
1 2:2 1 | 1 0.15 78.54
1 4 3:2 | 1 0.15 78.69
1 7:2 8 | 1 0.15 78.84
1 7:3 | 1 0.15 79.00
1 8 | 1 0.15 79.15
1 8 7 0 | 1 0.15 79.30
10 | 1 0.15 79.45
10 0:2 6 | 1 0.15 79.60
10 0:2 8 | 1 0.15 79.76
10 0:3 | 1 0.15 79.91
10 7 0 8 | 1 0.15 80.06
10:2 1:2 | 1 0.15 80.21
1:2 0 1 | 1 0.15 80.37
1:2 0 2 | 1 0.15 80.52
1:2 0:2 | 1 0.15 80.67
1:2 7 | 1 0.15 80.82
1:2 7 1 | 1 0.15 80.97
1:2 7 2 | 1 0.15 81.13
1:2 7:2 | 1 0.15 81.28
1:2 8 0 | 1 0.15 81.43
1:2 8 2 | 1 0.15 81.58
1:2 8:2 | 1 0.15 81.74
1:3 0 | 1 0.15 81.89
1:3 7 | 1 0.15 82.04
1:3 8 | 1 0.15 82.19
2 | 1 0.15 82.34
2 0 2 | 1 0.15 82.50
2 0 2 0 | 1 0.15 82.65
2 0:2 8 | 1 0.15 82.80
2 1 | 1 0.15 82.95
2 1 2:2 | 1 0.15 83.11
2 1 7 8 | 1 0.15 83.26
2 1 8 0 | 1 0.15 83.41
2 1 8 6 | 1 0.15 83.56
2 1 8:2 | 1 0.15 83.71
2 10 0 | 1 0.15 83.87
2 1:2 2 | 1 0.15 84.02
2 4 0 | 1 0.15 84.17
2 6 2 8 | 1 0.15 84.32
2 7 | 1 0.15 84.47
2 7:3 | 1 0.15 84.63
2 8 0:2 | 1 0.15 84.78
2 8 7:2 | 1 0.15 84.93
2 8:3 | 1 0.15 85.08
2:2 0 8 | 1 0.15 85.24
2:2 1 2 | 1 0.15 85.39
2:2 1 7 | 1 0.15 85.54
2:2 6 2 | 1 0.15 85.69
2:2 7 | 1 0.15 85.84
2:2 7 8 | 1 0.15 86.00
2:2 7:2 | 1 0.15 86.15
2:2 8 | 1 0.15 86.30
2:2 8 7 | 1 0.15 86.45
2:3 | 1 0.15 86.61
3 | 1 0.15 86.76
3 0 | 1 0.15 86.91
3 0:2 | 1 0.15 87.06
3 1 | 1 0.15 87.21
3 4 | 1 0.15 87.37
3 4 0 | 1 0.15 87.52
3 4 0:2 | 1 0.15 87.67
3 4 3 4 | 1 0.15 87.82
3 4 8 0 | 1 0.15 87.98
3 4:2 | 1 0.15 88.13
3 4:3 | 1 0.15 88.28
3 7:3 | 1 0.15 88.43
3 8 0 | 1 0.15 88.58
3:2 0 4 | 1 0.15 88.74
3:2 7 3 | 1 0.15 88.89
3:2 7:2 | 1 0.15 89.04
3:3 1 | 1 0.15 89.19
3:3 4 | 1 0.15 89.35
4 0 10 0 | 1 0.15 89.50
4 0 8 | 1 0.15 89.65
4 0:2 4 | 1 0.15 89.80
4 1:2 | 1 0.15 89.95
4 3 4 3 | 1 0.15 90.11
4 3 8:2 | 1 0.15 90.26
4 3:2 4 | 1 0.15 90.41
4 3:3 | 1 0.15 90.56
4 7 | 1 0.15 90.72
4 7 0 | 1 0.15 90.87
4 7:3 | 1 0.15 91.02
4 8 | 1 0.15 91.17
4 8 0:2 | 1 0.15 91.32
4 8 4 8 | 1 0.15 91.48
4:2 0 8 | 1 0.15 91.63
4:2 3 6 | 1 0.15 91.78
4:2 8:2 | 1 0.15 91.93
4:3 8 | 1 0.15 92.09
4:4 | 1 0.15 92.24
5 4 | 1 0.15 92.39
5 8 0 7 | 1 0.15 92.54
5 8 7:2 | 1 0.15 92.69
5 8:2 | 1 0.15 92.85
6 0 | 1 0.15 93.00
6 0:2 8 | 1 0.15 93.15
6 5 7 | 1 0.15 93.30
6 8 7 8 | 1 0.15 93.46
6 8:2 | 1 0.15 93.61
6 8:2 6 | 1 0.15 93.76
6:2 8 7 | 1 0.15 93.91
7 0 7 8 | 1 0.15 94.06
7 0 7:2 | 1 0.15 94.22
7 0 8:2 | 1 0.15 94.37
7 0:2 8 | 1 0.15 94.52
7 1 | 1 0.15 94.67
7 2 8 1 | 1 0.15 94.82
7 3 | 1 0.15 94.98
7 4 0:2 | 1 0.15 95.13
7 4 3 4 | 1 0.15 95.28
7 6 8:2 | 1 0.15 95.43
7 6:2 8 | 1 0.15 95.59
7 8:2 | 1 0.15 95.74
7 8:2 0 | 1 0.15 95.89
7:2 8 7 | 1 0.15 96.04
7:3 | 1 0.15 96.19
7:3 6 | 1 0.15 96.35
8 0 4:2 | 1 0.15 96.50
8 0 7:2 | 1 0.15 96.65
8 0 8 | 1 0.15 96.80
8 1 0 2 | 1 0.15 96.96
8 1:2 2 | 1 0.15 97.11
8 1:3 | 1 0.15 97.26
8 2 6 8 | 1 0.15 97.41
8 2 8:2 | 1 0.15 97.56
8 2:3 | 1 0.15 97.72
8 3 8:2 | 1 0.15 97.87
8 3:2 | 1 0.15 98.02
8 4 8 7 | 1 0.15 98.17
8 6 7 5 | 1 0.15 98.33
8 7 4 8 | 1 0.15 98.48
8 7 6 8 | 1 0.15 98.63
8 7:2 0 | 1 0.15 98.78
8:2 0:2 | 1 0.15 98.93
8:2 4 | 1 0.15 99.09
8:2 6 8 | 1 0.15 99.24
8:2 7 0 | 1 0.15 99.39
8:2 7 3 | 1 0.15 99.54
8:3 | 1 0.15 99.70
9 0:3 | 1 0.15 99.85
9 8 9 0 | 1 0.15 100.00
------------+-----------------------------------
Total | 657 100.00Ranking
*las 20 secuencias más frecuentes
sqtab, ranks(1/20)
sqtab if se1_1==0, ranks(1/20)
sqtab if se1_1==1, ranks(1/20)Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
0:4 | 100 20.20 20.20
0:2 | 71 14.34 34.55
0:3 | 32 6.46 41.01
8:4 | 29 5.86 46.87
3:4 | 28 5.66 52.53
1:4 | 22 4.44 56.97
7:4 | 21 4.24 61.21
0:3 8 | 19 3.84 65.05
0 8:3 | 18 3.64 68.69
0:2 8:2 | 18 3.64 72.32
8:2 | 17 3.43 75.76
1:2 | 15 3.03 78.79
8 0 | 13 2.63 81.41
0 8 | 12 2.42 83.84
2:4 | 12 2.42 86.26
8:2 7 8 | 11 2.22 88.48
0 | 10 2.02 90.51
0:2 8 0 | 10 2.02 92.53
8 0:3 | 10 2.02 94.55
0:2 8 | 9 1.82 96.36
8 0 8:2 | 9 1.82 98.18
8 7:3 | 9 1.82 100.00
------------+-----------------------------------
Total | 495 100.00
Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
0:4 | 42 18.34 18.34
0:2 | 31 13.54 31.88
0:3 | 15 6.55 38.43
3:4 | 13 5.68 44.10
8:4 | 13 5.68 49.78
0 8:3 | 11 4.80 54.59
7:4 | 11 4.80 59.39
8:2 | 10 4.37 63.76
0 8 | 7 3.06 66.81
1:2 | 7 3.06 69.87
1:4 | 6 2.62 72.49
2:4 | 6 2.62 75.11
8 0 | 6 2.62 77.73
8 0 8:2 | 6 2.62 80.35
0 | 5 2.18 82.53
0:2 8 | 5 2.18 84.72
0:2 8:2 | 5 2.18 86.90
0:3 8 | 5 2.18 89.08
2:2 8:2 | 5 2.18 91.27
7:2 | 5 2.18 93.45
7:2 8:2 | 5 2.18 95.63
8:2 7 8 | 5 2.18 97.82
8:3 | 5 2.18 100.00
------------+-----------------------------------
Total | 229 100.00
Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
0:4 | 58 20.28 20.28
0:2 | 40 13.99 34.27
0:3 | 17 5.94 40.21
1:4 | 16 5.59 45.80
8:4 | 16 5.59 51.40
3:4 | 15 5.24 56.64
0:3 8 | 14 4.90 61.54
0:2 8:2 | 13 4.55 66.08
7:4 | 10 3.50 69.58
1:2 | 8 2.80 72.38
8 0:3 | 8 2.80 75.17
0 8:3 | 7 2.45 77.62
0:2 8 0 | 7 2.45 80.07
8 0 | 7 2.45 82.52
8:2 | 7 2.45 84.97
2:4 | 6 2.10 87.06
8 7:3 | 6 2.10 89.16
8:2 7 8 | 6 2.10 91.26
0 | 5 1.75 93.01
0 8 | 5 1.75 94.76
2:2 0:2 | 5 1.75 96.50
7:3 8 | 5 1.75 98.25
8 7 8:2 | 5 1.75 100.00
------------+-----------------------------------
Total | 286 100.00Mismo orden vs mismo elementos
*SO secuencias con el mismo orden de elementossqtab, ranks(1/10) soSequence-Or |
der | Freq. Percent Cum.
------------+-----------------------------------
0 | 213 36.47 36.47
0 8 | 81 13.87 50.34
8 | 58 9.93 60.27
1 | 43 7.36 67.64
3 | 36 6.16 73.80
8 0 | 34 5.82 79.62
7 | 33 5.65 85.27
8 7 | 33 5.65 90.92
1 2 | 27 4.62 95.55
7 8 | 26 4.45 100.00
------------+-----------------------------------
Total | 584 100.00*SE secuencias con los mismos elementos
sqtab, ranks(1/10) seSequence-El |
ements | Freq. Percent Cum.
------------+-----------------------------------
0 | 213 27.13 27.13
0 8 | 160 20.38 47.52
7 8 | 101 12.87 60.38
8 | 58 7.39 67.77
0 2 | 50 6.37 74.14
0 7 8 | 49 6.24 80.38
1 | 43 5.48 85.86
1 2 | 42 5.35 91.21
3 | 36 4.59 95.80
7 | 33 4.20 100.00
------------+-----------------------------------
Total | 785 100.00Concentración:
*concentración
sqdes # of observed sequences: 1242
overall # of obs. elements: 11
max sequence length: 4
# of producible sequences: 14641
----------------------------------------------------------
Observations | Sequences % of observed Cum.
-------------+--------------------------------------------
1 | 262 21.09501 21.09501
2 | 66 5.31401 26.40902
3 | 36 2.898551 29.30757
4 | 16 1.288245 30.59581
5 | 14 1.127214 31.72303
6 | 10 .805153 32.52818
7 | 5 .4025765 32.93076
8 | 2 .1610306 33.09179
9 | 3 .2415459 33.33334
10 | 3 .2415459 33.57488
11 | 1 .0805153 33.6554
12 | 2 .1610306 33.81643
13 | 1 .0805153 33.89694
15 | 1 .0805153 33.97746
17 | 1 .0805153 34.05797
18 | 2 .1610306 34.219
19 | 1 .0805153 34.29952
21 | 1 .0805153 34.38003
22 | 1 .0805153 34.46055
28 | 1 .0805153 34.54107
29 | 1 .0805153 34.62158
32 | 1 .0805153 34.7021
71 | 1 .0805153 34.78261
100 | 1 .0805153 34.86312
|
Total | 433 34.86312
----------------------------------------------------------Descriptivo
*descripción de las secuencias
egen length=sqlength()
egen length1=sqlength(), element(1)
egen length2=sqlength(), element(2)
egen length3=sqlength(), element(3)
egen length4=sqlength(), element(4)
egen elemnum=sqelemcount()
egen epinum=sqepicount()
sqstatsum
sqstattab1 elemnum //las secuencias están compuestas principalmente de 2 o 3 elementos
sqstattabsum se1_1 Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
length | 1,242 3.433977 .8483221 1 4
length1 | 1,242 .2673108 .7718104 0 4
length2 | 1,242 .31562 .7650067 0 4
length3 | 1,242 .2294686 .7657039 0 4
length4 | 1,242 .1698873 .5454033 0 4
-------------+---------------------------------------------------------
elemnum | 1,242 1.87037 .7607037 1 4
epinum | 1,242 2.086151 .9774994 1 4
-> tabulation of elemnum
Number of |
different |
elements in |
sequence | Freq. Percent Cum.
------------+-----------------------------------
1 | 422 33.98 33.98
2 | 587 47.26 81.24
3 | 205 16.51 97.75
4 | 28 2.25 100.00
------------+-----------------------------------
Total | 1,242 100.00
| Summary of Length of sequence
Sexo | Mean Std. dev. Freq.
------------+------------------------------------
Masculino | 3.4 .86642076 585
Femenino | 3.4642314 .8313681 657
------------+------------------------------------
Total | 3.4339775 .84832211 1,242
| Summary of Length of episodes of
| element 1
Sexo | Mean Std. dev. Freq.
------------+------------------------------------
Masculino | .22735043 .68341707 585
Femenino | .30289193 .84167779 657
------------+------------------------------------
Total | .26731079 .77181036 1,242
| Summary of Length of episodes of
| element 2
Sexo | Mean Std. dev. Freq.
------------+------------------------------------
Masculino | .31965812 .77771171 585
Femenino | .31202435 .75408947 657
------------+------------------------------------
Total | .31561997 .76500671 1,242
| Summary of Length of episodes of
| element 3
Sexo | Mean Std. dev. Freq.
------------+------------------------------------
Masculino | .23931624 .76804293 585
Femenino | .22070015 .76409367 657
------------+------------------------------------
Total | .2294686 .76570395 1,242
| Summary of Length of episodes of
| element 4
Sexo | Mean Std. dev. Freq.
------------+------------------------------------
Masculino | .1965812 .6226231 585
Femenino | .14611872 .4652114 657
------------+------------------------------------
Total | .16988728 .54540331 1,242
| Summary of Number of different
| elements in sequence
Sexo | Mean Std. dev. Freq.
------------+------------------------------------
Masculino | 1.8700855 .75865609 585
Femenino | 1.870624 .76310007 657
------------+------------------------------------
Total | 1.8703704 .76070371 1,242
| Summary of Number of episodes
Sexo | Mean Std. dev. Freq.
------------+------------------------------------
Masculino | 2.0820513 .98103728 585
Femenino | 2.0898021 .97507166 657
------------+------------------------------------
Total | 2.0861514 .9774994 1,242Graficación de secuencias
Gráfico de índice, una linea por secuencia. Se ven todas
*gráficamente...
sqindexplot, ///
ytitle(Secuencias) xtitle(Ronda) xlabel(1(1)4) ///
graphregion(fcolor(white))
Trayectorias:
sqparcoord, ylabel(0(1)10, valuelabel angle(0)) wlines(2)
Comparación de secuencias: Optimal matching
Con costos default: indel=1,sub=2
sqom, full
*** OJO ESTO SE TIENE QUE CORRER TODO JUNTO
sqclusterdat
clustermat wardslinkage SQdist, name(wards) add
cluster tree wards, cutnumber(10)
cluster generate cluster5 = groups(5) // hacemos 5 grupos de acuerdo al grafico
sqclusterdat, returnPerform 93528 Comparisons with Needleman-Wunsch Algorithm
Running mata function
Distance matrix saved as SQdist
Es muy util, dado el formato long, que hagamos algunos indicadores internos:
bysort id: gen n_ent=_n
tab cluster5 if n_ent==1
cluster5 | Freq. Percent Cum.
------------+-----------------------------------
1 | 273 21.98 21.98
2 | 476 38.33 60.31
3 | 102 8.21 68.52
4 | 261 21.01 89.53
5 | 130 10.47 100.00
------------+-----------------------------------
Total | 1,242 100.00¿Qué hay dentro de los clúster
Podemos encontrar la secuencia que más se repite dentro de cada cluster. Esto nos puede ayudar a darle nombre a nuestros cluster y por tanto a las tipologías de las variables
*Tipo ideal de trayectoria por cluster
sqmodalplot, by(cluster5) graphregion(fcolor(white))
More than 1 mode found at some positions; tie(gap) applied
*por sexo
sqmodalplot, over(cluster5) by(se1_1) graphregion(fcolor(white))
More than 1 mode found at some positions; tie(gap) applied
También podemos hacer un indexplot:
sqindexplot, by(cluster5)
También podemos tabular su interior:
sqtab if cluster5==1
sqtab if cluster5==2
sqtab if cluster5==3
sqtab if cluster5==4
sqtab if cluster5==5Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
0:4 | 100 36.63 36.63
0:2 | 71 26.01 62.64
0:3 | 32 11.72 74.36
0:3 8 | 19 6.96 81.32
8 0:3 | 10 3.66 84.98
0 4 0:2 | 4 1.47 86.45
4 0:3 | 4 1.47 87.91
7 0:3 | 3 1.10 89.01
0 6 0:2 | 2 0.73 89.74
0:2 10 | 2 0.73 90.48
0:3 4 | 2 0.73 91.21
4 0:2 | 2 0.73 91.94
7:2 0:2 | 2 0.73 92.67
0 10 0:2 | 1 0.37 93.04
0 3 0 4 | 1 0.37 93.41
0 3 0:2 | 1 0.37 93.77
0:2 10 0 | 1 0.37 94.14
0:2 9 6 | 1 0.37 94.51
0:3 1 | 1 0.37 94.87
0:3 10 | 1 0.37 95.24
0:3 2 | 1 0.37 95.60
10 0:2 6 | 1 0.37 95.97
10 0:3 | 1 0.37 96.34
3 0:2 | 1 0.37 96.70
3 4 0:2 | 1 0.37 97.07
3:2 0:2 | 1 0.37 97.44
4 0 10 0 | 1 0.37 97.80
4 0:2 4 | 1 0.37 98.17
4 3 0:2 | 1 0.37 98.53
6 0:2 | 1 0.37 98.90
6 0:2 8 | 1 0.37 99.27
7 4 0:2 | 1 0.37 99.63
9 0:3 | 1 0.37 100.00
------------+-----------------------------------
Total | 273 100.00
Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
8:4 | 29 6.09 6.09
0 8:3 | 18 3.78 9.87
0:2 8:2 | 18 3.78 13.66
8:2 | 17 3.57 17.23
8 0 | 13 2.73 19.96
0 8 | 12 2.52 22.48
8:2 7 8 | 11 2.31 24.79
0 | 10 2.10 26.89
0:2 8 0 | 10 2.10 28.99
0:2 8 | 9 1.89 30.88
8 0 8:2 | 9 1.89 32.77
7:2 8:2 | 8 1.68 34.45
8:2 7 | 7 1.47 35.92
0 8 0:2 | 6 1.26 37.18
7 8:3 | 6 1.26 38.45
8 | 6 1.26 39.71
8 7 8:2 | 6 1.26 40.97
8:3 | 6 1.26 42.23
0 8 0 8 | 5 1.05 43.28
0 8:2 | 5 1.05 44.33
0:2 7 8 | 5 1.05 45.38
7 8 7 8 | 5 1.05 46.43
8 0 8 0 | 5 1.05 47.48
8 7 | 5 1.05 48.53
8:3 0 | 5 1.05 49.58
2 8 | 4 0.84 50.42
7 0 | 4 0.84 51.26
7 8:2 7 | 4 0.84 52.10
8 0 7 8 | 4 0.84 52.94
8 0:2 | 4 0.84 53.78
8:3 7 | 4 0.84 54.62
0 2 0 8 | 3 0.63 55.25
0 2 8 0 | 3 0.63 55.88
0 7 | 3 0.63 56.51
0 7 8 | 3 0.63 57.14
0 7 8:2 | 3 0.63 57.77
0 8 0 | 3 0.63 58.40
0 8 7 8 | 3 0.63 59.03
0 8:2 7 | 3 0.63 59.66
2 0 8 | 3 0.63 60.29
2 8:3 | 3 0.63 60.92
7 0 7 8 | 3 0.63 61.55
7 8 | 3 0.63 62.18
8 0 8 | 3 0.63 62.82
8 4 8:2 | 3 0.63 63.45
8 7 8 7 | 3 0.63 64.08
0 2 8 | 2 0.42 64.50
0 2 8:2 | 2 0.42 64.92
0 4:2 0 | 2 0.42 65.34
0 7 8 0 | 2 0.42 65.76
0 7:2 8 | 2 0.42 66.18
0 8 7 | 2 0.42 66.60
0:2 7 | 2 0.42 67.02
0:2 7 0 | 2 0.42 67.44
0:2 7:2 | 2 0.42 67.86
0:2 8 4 | 2 0.42 68.28
2 0 7 8 | 2 0.42 68.70
2 0 8:2 | 2 0.42 69.12
2 0:2 8 | 2 0.42 69.54
2 8:2 | 2 0.42 69.96
3 8:3 | 2 0.42 70.38
4 8 0:2 | 2 0.42 70.80
6:2 8:2 | 2 0.42 71.22
7 | 2 0.42 71.64
7 0 7 | 2 0.42 72.06
7 0:2 8 | 2 0.42 72.48
7 8:2 | 2 0.42 72.90
8 0 7 0 | 2 0.42 73.32
8 0:2 8 | 2 0.42 73.74
8 2 8:2 | 2 0.42 74.16
8 3 | 2 0.42 74.58
8 4 8 7 | 2 0.42 75.00
8:2 0 7 | 2 0.42 75.42
8:2 0:2 | 2 0.42 75.84
0 10 0 8 | 1 0.21 76.05
0 10 7 | 1 0.21 76.26
0 10 7 8 | 1 0.21 76.47
0 10 8:2 | 1 0.21 76.68
0 2 7 8 | 1 0.21 76.89
0 3 | 1 0.21 77.10
0 3 0 7 | 1 0.21 77.31
0 3 7 | 1 0.21 77.52
0 3 8 7 | 1 0.21 77.73
0 3 8:2 | 1 0.21 77.94
0 4 | 1 0.21 78.15
0 4 0 | 1 0.21 78.36
0 4 0 8 | 1 0.21 78.57
0 4 3 | 1 0.21 78.78
0 4 7 0 | 1 0.21 78.99
0 4 8 | 1 0.21 79.20
0 4 8 7 | 1 0.21 79.41
0 4:2 | 1 0.21 79.62
0 5 8 0 | 1 0.21 79.83
0 6 | 1 0.21 80.04
0 6 0 7 | 1 0.21 80.25
0 6 0 8 | 1 0.21 80.46
0 6 8:2 | 1 0.21 80.67
0 7 0 | 1 0.21 80.88
0 7 0 8 | 1 0.21 81.09
0 7 8 6 | 1 0.21 81.30
0 8 3 5 | 1 0.21 81.51
0 8 5 8 | 1 0.21 81.72
0 8 6 0 | 1 0.21 81.93
0 8 6:2 | 1 0.21 82.14
0 8:2 0 | 1 0.21 82.35
0 8:2 6 | 1 0.21 82.56
0:2 2 8 | 1 0.21 82.77
0:2 3:2 | 1 0.21 82.98
0:2 8 2 | 1 0.21 83.19
0:2 8 7 | 1 0.21 83.40
0:3 7 | 1 0.21 83.61
1 0 8 0 | 1 0.21 83.82
1 0 8:2 | 1 0.21 84.03
1 2 0 8 | 1 0.21 84.24
1 2 4 8 | 1 0.21 84.45
1 2 8:2 | 1 0.21 84.66
1 4 7 8 | 1 0.21 84.87
1 7:2 8 | 1 0.21 85.08
10 | 1 0.21 85.29
10 0 | 1 0.21 85.50
10 0 4 0 | 1 0.21 85.71
10 0 8 | 1 0.21 85.92
10 0:2 8 | 1 0.21 86.13
10 7 0 8 | 1 0.21 86.34
10:2 0 8 | 1 0.21 86.55
2 1 7 8 | 1 0.21 86.76
2 1 8:2 | 1 0.21 86.97
2 10 | 1 0.21 87.18
2 10 0 | 1 0.21 87.39
2 6 7 8 | 1 0.21 87.61
2 8 0 8 | 1 0.21 87.82
3 4 8:2 | 1 0.21 88.03
4 3 8:2 | 1 0.21 88.24
4 6:3 | 1 0.21 88.45
4 7 | 1 0.21 88.66
4 7 0 | 1 0.21 88.87
4 8 0 8 | 1 0.21 89.08
4 8 4 8 | 1 0.21 89.29
4 8:2 | 1 0.21 89.50
4:2 8:2 | 1 0.21 89.71
5 0 | 1 0.21 89.92
5 7 | 1 0.21 90.13
5 7 5 | 1 0.21 90.34
5 8 0 7 | 1 0.21 90.55
5 8:2 | 1 0.21 90.76
6 | 1 0.21 90.97
6 0 | 1 0.21 91.18
6 5 7 | 1 0.21 91.39
6 5 7 1 | 1 0.21 91.60
6 7 8:2 | 1 0.21 91.81
6 7:2 8 | 1 0.21 92.02
6 8 0 8 | 1 0.21 92.23
6 8 6 8 | 1 0.21 92.44
6 8 7 8 | 1 0.21 92.65
6 8:2 | 1 0.21 92.86
6 8:2 6 | 1 0.21 93.07
6:2 7 | 1 0.21 93.28
6:2 8 6 | 1 0.21 93.49
6:2 8 7 | 1 0.21 93.70
7 0 8:2 | 1 0.21 93.91
7 1 | 1 0.21 94.12
7 2 8 1 | 1 0.21 94.33
7 6 8:2 | 1 0.21 94.54
7 6:2 8 | 1 0.21 94.75
7 8 0:2 | 1 0.21 94.96
7 8:2 0 | 1 0.21 95.17
8 0 4 | 1 0.21 95.38
8 0 4:2 | 1 0.21 95.59
8 1 2 8 | 1 0.21 95.80
8 10 8 0 | 1 0.21 96.01
8 2 6 8 | 1 0.21 96.22
8 3 8:2 | 1 0.21 96.43
8 3:2 | 1 0.21 96.64
8 6 2 | 1 0.21 96.85
8 6 7 5 | 1 0.21 97.06
8 6 8:2 | 1 0.21 97.27
8 7 4 8 | 1 0.21 97.48
8 7 6 8 | 1 0.21 97.69
8 7 8 | 1 0.21 97.90
8 7 8 0 | 1 0.21 98.11
8:2 0 2 | 1 0.21 98.32
8:2 0 8 | 1 0.21 98.53
8:2 4 | 1 0.21 98.74
8:2 5 6 | 1 0.21 98.95
8:2 6 8 | 1 0.21 99.16
8:2 7 0 | 1 0.21 99.37
8:2 7 3 | 1 0.21 99.58
8:3 3 | 1 0.21 99.79
9 8 9 0 | 1 0.21 100.00
------------+-----------------------------------
Total | 476 100.00
Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
7:4 | 21 20.59 20.59
8 7:3 | 9 8.82 29.41
7:2 | 8 7.84 37.25
7:3 8 | 7 6.86 44.12
8 7:2 | 5 4.90 49.02
0 7:3 | 4 3.92 52.94
7 8 7 | 4 3.92 56.86
1 7:3 | 3 2.94 59.80
3:2 7:2 | 3 2.94 62.75
7 0 7:2 | 3 2.94 65.69
7:2 8 7 | 3 2.94 68.63
8 7:2 8 | 3 2.94 71.57
8:2 7:2 | 3 2.94 74.51
0 7 8 7 | 2 1.96 76.47
0 7:2 | 2 1.96 78.43
3 7:3 | 2 1.96 80.39
4:2 7:2 | 2 1.96 82.35
7 8 7:2 | 2 1.96 84.31
7:3 | 2 1.96 86.27
8 7:2 0 | 2 1.96 88.24
0 5 7:2 | 1 0.98 89.22
0 7:2 0 | 1 0.98 90.20
0 8 7:2 | 1 0.98 91.18
1 8 7:2 | 1 0.98 92.16
2 7:3 | 1 0.98 93.14
4 7:2 | 1 0.98 94.12
4 7:3 | 1 0.98 95.10
5 8 7:2 | 1 0.98 96.08
7 4 7:2 | 1 0.98 97.06
7:3 6 | 1 0.98 98.04
8 0 7:2 | 1 0.98 99.02
8 3 7:2 | 1 0.98 100.00
------------+-----------------------------------
Total | 102 100.00
Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
1:4 | 22 8.43 8.43
1:2 | 15 5.75 14.18
2:4 | 12 4.60 18.77
0 2 0:2 | 7 2.68 21.46
1 2:3 | 7 2.68 24.14
2:2 8:2 | 7 2.68 26.82
2 0 | 6 2.30 29.12
2:2 | 6 2.30 31.42
2:2 0:2 | 6 2.30 33.72
1:2 2 | 5 1.92 35.63
1:3 | 5 1.92 37.55
1:3 2 | 5 1.92 39.46
2:3 | 5 1.92 41.38
1 2:2 | 4 1.53 42.91
1:2 2:2 | 4 1.53 44.44
1:2 8:2 | 4 1.53 45.98
2:3 8 | 4 1.53 47.51
0 2 | 3 1.15 48.66
0 2:3 | 3 1.15 49.81
0:2 2 0 | 3 1.15 50.96
1 0 | 3 1.15 52.11
1:2 7:2 | 3 1.15 53.26
2 0:2 | 3 1.15 54.41
2 1 | 3 1.15 55.56
2:2 0 | 3 1.15 56.70
2:2 0 2 | 3 1.15 57.85
2:2 8 7 | 3 1.15 59.00
2:3 0 | 3 1.15 60.15
0 1 | 2 0.77 60.92
0 1 0:2 | 2 0.77 61.69
0 1 2:2 | 2 0.77 62.45
1 0:3 | 2 0.77 63.22
1 2 | 2 0.77 63.98
1 2 0 2 | 2 0.77 64.75
1 2 0:2 | 2 0.77 65.52
1:2 2 1 | 2 0.77 66.28
1:2 6 5 | 2 0.77 67.05
1:3 0 | 2 0.77 67.82
1:3 8 | 2 0.77 68.58
2 0 2 0 | 2 0.77 69.35
2 0:3 | 2 0.77 70.11
2 1:2 2 | 2 0.77 70.88
2 7 | 2 0.77 71.65
2:2 0 8 | 2 0.77 72.41
4 1:2 | 2 0.77 73.18
0 1 0 | 1 0.38 73.56
0 1 0 8 | 1 0.38 73.95
0 1 2 | 1 0.38 74.33
0 1 8 | 1 0.38 74.71
0 2 0 | 1 0.38 75.10
0 2 0 1 | 1 0.38 75.48
0 2 0 2 | 1 0.38 75.86
0 2 0 6 | 1 0.38 76.25
0 2 1 | 1 0.38 76.63
0 2 6 2 | 1 0.38 77.01
0 2:2 | 1 0.38 77.39
0 2:2 0 | 1 0.38 77.78
0 2:2 8 | 1 0.38 78.16
1 | 1 0.38 78.54
1 0 2 | 1 0.38 78.93
1 2 0 | 1 0.38 79.31
1 2 1 2 | 1 0.38 79.69
1 2 1:2 | 1 0.38 80.08
1 2 7 2 | 1 0.38 80.46
1 2 8 2 | 1 0.38 80.84
1 2:2 1 | 1 0.38 81.23
1 7 1 6 | 1 0.38 81.61
1 8 | 1 0.38 81.99
1 8 7 0 | 1 0.38 82.38
10:2 1:2 | 1 0.38 82.76
1:2 0 1 | 1 0.38 83.14
1:2 0 2 | 1 0.38 83.52
1:2 0:2 | 1 0.38 83.91
1:2 2 0 | 1 0.38 84.29
1:2 2 8 | 1 0.38 84.67
1:2 7 | 1 0.38 85.06
1:2 7 1 | 1 0.38 85.44
1:2 7 2 | 1 0.38 85.82
1:2 8 | 1 0.38 86.21
1:2 8 0 | 1 0.38 86.59
1:2 8 2 | 1 0.38 86.97
1:3 7 | 1 0.38 87.36
2 | 1 0.38 87.74
2 0 2 | 1 0.38 88.12
2 0 4 0 | 1 0.38 88.51
2 1 2 | 1 0.38 88.89
2 1 2 1 | 1 0.38 89.27
2 1 2:2 | 1 0.38 89.66
2 1 6 | 1 0.38 90.04
2 1 8 0 | 1 0.38 90.42
2 1 8 6 | 1 0.38 90.80
2 1:2 8 | 1 0.38 91.19
2 4 0 | 1 0.38 91.57
2 4 1 2 | 1 0.38 91.95
2 5 | 1 0.38 92.34
2 6 2 8 | 1 0.38 92.72
2 8 0:2 | 1 0.38 93.10
2 8 7 | 1 0.38 93.49
2 8 7:2 | 1 0.38 93.87
2:2 1 2 | 1 0.38 94.25
2:2 1 7 | 1 0.38 94.64
2:2 6 2 | 1 0.38 95.02
2:2 7 | 1 0.38 95.40
2:2 7 8 | 1 0.38 95.79
2:2 7:2 | 1 0.38 96.17
2:2 8 | 1 0.38 96.55
2:2 8 2 | 1 0.38 96.93
2:3 1 | 1 0.38 97.32
3 1 | 1 0.38 97.70
6 2:2 8 | 1 0.38 98.08
8 1 0 2 | 1 0.38 98.47
8 1:2 2 | 1 0.38 98.85
8 1:3 | 1 0.38 99.23
8 2 0 2 | 1 0.38 99.62
8 2:3 | 1 0.38 100.00
------------+-----------------------------------
Total | 261 100.00
Sequence-Pa |
ttern | Freq. Percent Cum.
------------+-----------------------------------
3:4 | 28 21.54 21.54
3:2 4 3 | 6 4.62 26.15
4:2 | 6 4.62 30.77
3:2 | 5 3.85 34.62
4:4 | 5 3.85 38.46
3:3 4 | 4 3.08 41.54
7 3 | 4 3.08 44.62
3:2 4 | 3 2.31 46.92
3:3 0 | 3 2.31 49.23
4 3 | 3 2.31 51.54
3 4 0 8 | 2 1.54 53.08
3 4:2 | 2 1.54 54.62
3 4:3 | 2 1.54 56.15
3 8 | 2 1.54 57.69
3:2 4:2 | 2 1.54 59.23
3:3 | 2 1.54 60.77
4 3 4:2 | 2 1.54 62.31
4:3 0 | 2 1.54 63.85
8 4:3 | 2 1.54 65.38
0 3 8 0 | 1 0.77 66.15
0 3:2 4 | 1 0.77 66.92
0 3:3 | 1 0.77 67.69
0 4 3:2 | 1 0.77 68.46
1 4 3:2 | 1 0.77 69.23
2 3 4 | 1 0.77 70.00
2 4 3 8 | 1 0.77 70.77
2 4:2 3 | 1 0.77 71.54
3 | 1 0.77 72.31
3 0 | 1 0.77 73.08
3 4 | 1 0.77 73.85
3 4 0 | 1 0.77 74.62
3 4 0 4 | 1 0.77 75.38
3 4 3 4 | 1 0.77 76.15
3 4 8 0 | 1 0.77 76.92
3 8 0 | 1 0.77 77.69
3:2 0 4 | 1 0.77 78.46
3:2 4 8 | 1 0.77 79.23
3:2 7 3 | 1 0.77 80.00
3:3 1 | 1 0.77 80.77
3:3 8 | 1 0.77 81.54
4 | 1 0.77 82.31
4 0 | 1 0.77 83.08
4 0 8 | 1 0.77 83.85
4 3 4 0 | 1 0.77 84.62
4 3 4 3 | 1 0.77 85.38
4 3 8 | 1 0.77 86.15
4 3 8 7 | 1 0.77 86.92
4 3:2 | 1 0.77 87.69
4 3:2 4 | 1 0.77 88.46
4 3:3 | 1 0.77 89.23
4 8 | 1 0.77 90.00
4:2 0 4 | 1 0.77 90.77
4:2 0 8 | 1 0.77 91.54
4:2 3 6 | 1 0.77 92.31
4:2 3:2 | 1 0.77 93.08
4:2 8 4 | 1 0.77 93.85
4:3 | 1 0.77 94.62
4:3 8 | 1 0.77 95.38
5 4 | 1 0.77 96.15
5 4 3 4 | 1 0.77 96.92
5 4:2 8 | 1 0.77 97.69
7 3 8 4 | 1 0.77 98.46
7 4 3 | 1 0.77 99.23
7 4 3 4 | 1 0.77 100.00
------------+-----------------------------------
Total | 130 100.00Aplicación de las secuencias
¿Qué define que algunas personas tengan unas trayectorias de identidad partidaria y otras?
Hacemos un multilogit:
recode se4_1 (99=.)
mlogit cluster5 i.se1_1 se2_ se4_1 if n_ent==1
estimates store mlogit
estadd fitstat(6 changes made to se4_1)
Iteration 0: log likelihood = -1821.083
Iteration 1: log likelihood = -1785.8622
Iteration 2: log likelihood = -1785.5173
Iteration 3: log likelihood = -1785.5171
Iteration 4: log likelihood = -1785.5171
Multinomial logistic regression Number of obs = 1,240
LR chi2(12) = 71.13
Prob > chi2 = 0.0000
Log likelihood = -1785.5171 Pseudo R2 = 0.0195
------------------------------------------------------------------------------
cluster5 | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
1 |
se1_1 |
Femenino | .4826684 .1568056 3.08 0.002 .1753352 .7900017
se2_ | .0124333 .0048851 2.55 0.011 .0028586 .0220079
se4_1 | .043819 .0182045 2.41 0.016 .0081389 .0794992
_cons | -1.683068 .3249921 -5.18 0.000 -2.320041 -1.046095
-------------+----------------------------------------------------------------
2 | (base outcome)
-------------+----------------------------------------------------------------
3 |
se1_1 |
Femenino | .0196463 .2228605 0.09 0.930 -.4171523 .4564449
se2_ | .0246671 .0066706 3.70 0.000 .011593 .0377413
se4_1 | .0388988 .0252905 1.54 0.124 -.0106697 .0884674
_cons | -2.893028 .4615213 -6.27 0.000 -3.797593 -1.988463
-------------+----------------------------------------------------------------
4 |
se1_1 |
Femenino | .2236207 .1586186 1.41 0.159 -.087266 .5345074
se2_ | .0156911 .0049177 3.19 0.001 .0060526 .0253295
se4_1 | -.0250579 .0186168 -1.35 0.178 -.0615462 .0114304
_cons | -1.164667 .3288827 -3.54 0.000 -1.809265 -.5200686
-------------+----------------------------------------------------------------
5 |
se1_1 |
Femenino | -.0321322 .2060244 -0.16 0.876 -.4359325 .3716682
se2_ | .0340292 .0061547 5.53 0.000 .0219662 .0460923
se4_1 | .003339 .0232512 0.14 0.886 -.0422324 .0489105
_cons | -2.799438 .4317647 -6.48 0.000 -3.645681 -1.953194
------------------------------------------------------------------------------
Measures of Fit for mlogit of cluster5
Log-Lik Intercept Only: -1821.083 Log-Lik Full Model: -1785.517
D(1215): 3571.034 LR(12): 71.132
Prob > LR: 0.000
McFadden's R2: 0.020 McFadden's Adj R2: 0.006
Maximum Likelihood R2: 0.056 Cragg & Uhler's R2: 0.059
Count R2: 0.388 Adj Count R2: 0.007
AIC: 2.920 AIC*n: 3621.034
BIC: -5083.249 BIC': 14.342
added scalars:
e(dev) = 3571.0341
e(dev_df) = 1215
e(lrx2) = 71.131921
e(lrx2_df) = 12
e(lrx2_p) = 1.966e-10
e(r2_mf) = .01953011
e(r2_mfadj) = .00580202
e(r2_ml) = .05575013
e(r2_cu) = .05887102
e(r2_ct) = .38790323
e(r2_ctadj) = .0065445
e(aic0) = 2.9201888
e(aic_n) = 3621.0341
e(bic0) = -5083.2488
e(bic_p) = 14.342479
e(n_rhs) = 24
e(n_parm) = 25Para poder intrerpretar mejor hacemos los margins, con un loop para cada respuesta de la variable dependiente
foreach o in 1 2 3 4 5 {
margins, dydx(*) predict(outcome(`o')) post
estimates store out`o', title(Outcome `o')
estimates restore mlogit
} 2. margins, dydx(*) predict(outcome(`o')) post
3. estimates store out`o', title(Outcome `o')
4. estimates restore mlogit
5. }
Average marginal effects Number of obs = 1,240
Model VCE: OIM
Expression: Pr(cluster5==1), predict(outcome(1))
dy/dx wrt: 1.se1_1 se2_ se4_1
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
se1_1 |
Femenino | .0716804 .0235556 3.04 0.002 .0255123 .1178486
se2_ | .0002094 .0007151 0.29 0.770 -.0011922 .001611
se4_1 | .0077849 .0027157 2.87 0.004 .0024622 .0131076
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
(results mlogit are active now)
Average marginal effects Number of obs = 1,240
Model VCE: OIM
Expression: Pr(cluster5==2), predict(outcome(2))
dy/dx wrt: 1.se1_1 se2_ se4_1
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
se1_1 |
Femenino | -.0577896 .0277728 -2.08 0.037 -.1122233 -.003356
se2_ | -.0042946 .0008365 -5.13 0.000 -.0059341 -.002655
se4_1 | -.003025 .0032463 -0.93 0.351 -.0093877 .0033377
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
(results mlogit are active now)
Average marginal effects Number of obs = 1,240
Model VCE: OIM
Expression: Pr(cluster5==3), predict(outcome(3))
dy/dx wrt: 1.se1_1 se2_ se4_1
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
se1_1 |
Femenino | -.0108397 .0158185 -0.69 0.493 -.0418434 .020164
se2_ | .0010452 .0004582 2.28 0.023 .0001471 .0019433
se4_1 | .0025312 .0017648 1.43 0.151 -.0009278 .0059902
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
(results mlogit are active now)
Average marginal effects Number of obs = 1,240
Model VCE: OIM
Expression: Pr(cluster5==4), predict(outcome(4))
dy/dx wrt: 1.se1_1 se2_ se4_1
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
se1_1 |
Femenino | .0156862 .0234332 0.67 0.503 -.030242 .0616144
se2_ | .0008147 .0006981 1.17 0.243 -.0005537 .002183
se4_1 | -.0068562 .0027085 -2.53 0.011 -.0121648 -.0015476
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
(results mlogit are active now)
Average marginal effects Number of obs = 1,240
Model VCE: OIM
Expression: Pr(cluster5==5), predict(outcome(5))
dy/dx wrt: 1.se1_1 se2_ se4_1
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
se1_1 |
Femenino | -.0187373 .0174404 -1.07 0.283 -.0529198 .0154451
se2_ | .0022253 .0005116 4.35 0.000 .0012226 .0032279
se4_1 | -.0004349 .0019197 -0.23 0.821 -.0041973 .0033276
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
(results mlogit are active now)Y a graficar:
coefplot out1 out2 out3 out4 out5 , xline(0) xline(0) msymbol(d) ///
mfcolor(white) > mfcolor(white) Bibliografía para seguirle
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- Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and Visualizing State Sequences in R with TraMineR. Journal of Statistical Software, 40(4). https://doi.org/10.18637/jss.v040.i04
- Gauthier, J.-A., Widmer, E. D., Bucher, P., & Notredame, C. (2010). 1. Multichannel Sequence Analysis Applied to Social Science Data. Sociological Methodology, 40(1), 1–38. https://doi.org/10.1111/j.1467-9531.2010.01227.x
- Halpin, B. (2017). SADI: Sequence Analysis Tools for Stata. The Stata Journal: Promoting Communications on Statistics and Stata, 17(3), 546–572. https://doi.org/10.1177/1536867X1701700302
- Kohler, U., Luniak, M., & Brzinsky-Fay, C. (2016). SQ: Stata module for sequence analysis. Boston College Department of Economics. Recuperado de https://ideas.repec.org/c/boc/bocode/s456755.html
- Rossignon, F., Studer, M., Gauthier, J.-A., & Gof* f, J.-M. L. (2018). Sequence History Analysis (SHA): Estimating the Effect of Past Trajectories on an Upcoming Event. En G. Ritschard & M. Studer (Eds.), Sequence Analysis and Related Approaches (pp. 83–100). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-95420-2_6